BackgroundMetastasis-associated protein 1 (MTA1) has been considered as a transcriptional regulator, which is significantly related to the prognosis in various types of tumors. However, whether MTA1 is a potential prognostic index of gastrointestinal cancer (GIC) remains controversial. The current meta-analysis was performed to evaluate the role of MTA1 expression in the prediction of the clinicopathological features and survival in GIC cases. And the results of gastric cancer were verified by immunohistochemistry (IHC).MethodsEligible studies assessing the relationship between MTA1 and GIC by IHC were searched in the PubMed, Cochrane, Ovid, Web of Science and CNKI databases by various search strategies. The STATA 16.0 software was applied to gather data and to analyze the potential relationship between MTA1 and GIC. The expression level of MTA1 was examined in 80 GC samples by IHC assay. SPSS 20.0 was applied for statistical analysis, and the survival curves were calculated by the Kaplan-Meier method. The data of 95% CI was displayed as “[a-b]”.ResultsAccording to the meta-analysis, the expression level of MTA1 was tightly associated with the tumor size (OR=1.82 [1.16–2.84], P=0.009), tumor tissue differentiation (OR=1.71 [1.24–2.37], P=0.001), depth of invasion (OR=3.12 [2.55–3.83], P<0.001), lymphatic metastasis (OR=2.99 [2.02–4.43], P<0.001), distant metastasis (OR=4.66 [1.13–19.24], P=0.034), TNM stage (OR=4.28 [2.76–6.63], P<0.001). In addition, MTA1 played the negative effects in 1- (RR=2.48 [1.45–4.25], P=0.001), 3- (RR=1.66 [1.30–2.11], P<0.001) and 5-year (RR=1.73 [1.37–2.20], P<0.001). Study in subgroup, grouped by language and tumor type, we reached similar conclusions. Further validation by IHC yielded similar conclusions. Tumor size (P=0.008), lymph node metastasis (P=0.007) and distant metastasis (P=0.023) significantly accompanied with higher expression of MAT1 in GC cases. Besides, the expression level of MTA1 was statistically significantly correlated with OS in GC cases (HR=2.061 [1.066–3.986], P=0.032), which suggested that MTA1 might be an independent prognostic marker for GC. Finally, we verified the correlation between the expression level of MTA1 and prognosis of GC in 80 GC samples.ConclusionsMTA1 is tightly associated with metastasis-related factors and may constitute a promising prognostic factor of GIC.
Background: Gastric cancer (GC) is one of the most pravelent cancer in the world. Although increasing studies have indicated that autophagy-related long non-coding RNA (lncRNA) plays an essential role in the occurrence of GC, the prognosis of GC based on autophagy is still deficient.Method: Autophagy-related lncRNAs were obtained by using the correlation test with the autophagy-related gene. Data was downloaded from The Cancer Genome of Atlas stomach adenocarcinoma (TCGA-STAD) dataset. The prognostic autophagy-related lncRNAs significantly correlated with survival of TCGA-STAD dataset were obtained by using Kaplan-Meier and univariate Cox regression analysis. TCGA-STAD dataset was separated into a training set and a testing set randomly. The model was constructed based on the training set through the least absolute shrinkage and selection operator (LASSO) regression. The testing set and TCGA-STAD were used to validate the accuracy of the model. Every patient got a risk score (RS) and patients were separate into high-risk group and low-risk group due to the median RS. The prognostic network was built and the mRNAs in the system were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The signaling pathways that the differentially expressed genes (DEGs) between two types of risk group mainly participated in were distinguished through Gene Set Enrichment Analysis (GSEA). The individual’s survival rate was predicted through the nomogram.Results: 24 autophagy-related lncRNAs were found strongly associated with the survival of the TCGA-STAD dataset. Among them, 11 lncRNAs were selected to build the risk score model through LASSO regression. The multivariate Cox analysis showed that the RS could be an independent prognosis predictor. The Kaplan-Meier survival analysis and the Receiver Operating Characteristic (ROC) curve indicated the model had an excellent prediction effect. GO, and KEGG analysis revealed that the mRNAs in the prognostic network were mainly involved in the autophagy and ubiquitin-like protein ligase binding. GSEA analysis uncovered that the DEGs in high-risk group partially participated in the ECM receptor interaction and other signaling pathways.Conclusions: Our results indicated that the risk score model based on the autophagy-related lncRNAs performed well in the prediction of prognosis for patients with GC.
Gastric cancer (GC) is one of the most common cancer worldwide. Although emerging evidence indicates that autophagy-related long non-coding RNA (lncRNA) plays an important role in the progression of GC, the prognosis of GC based on autophagy is still deficient. The Cancer Genome of Atlas stomach adenocarcinoma (TCGA-STAD) dataset was downloaded and separated into a training set and a testing set randomly. Then, 24 autophagy-related lncRNAs were found strongly associated with the survival of the TCGA-STAD dataset. 11 lncRNAs were selected to build the risk score model through the least absolute shrinkage and selection operator (LASSO) regression. Every patient got a risk score (RS), and patients were separated into a high-risk group and a low-risk group due to the median RS. The multivariate Cox analysis showed that the RS could be an independent prognosis predictor. The Kaplan-Meier survival analysis and the Receiver Operating Characteristic (ROC) curve indicated the model had an excellent prediction effect. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the mRNAs in the prognostic network were mainly involved in the autophagy and ubiquitin-like protein ligase binding. Gene Set Enrichment Analysis (GSEA) analysis uncovered that the differentially expressed genes (DEGs) in the high-risk group partially participated in the ECM receptor interaction and other signaling pathways. Our results indicated that the risk score model based on the autophagy-related lncRNAs performed well in the prediction of prognosis for patients with GC.
Gastric cancer (GC) is one of the most common cancer worldwide. Although emerging evidence indicates that autophagy-related long non-coding RNA (lncRNA) plays an important role in the progression of GC, the prognosis of GC based on autophagy is still deficient. The Cancer Genome of Atlas stomach adenocarcinoma (TCGA-STAD) dataset was downloaded and separated into a training set and a testing set randomly. Then, 24 autophagy-related lncRNAs were found strongly associated with the survival of the TCGA-STAD dataset. 11 lncRNAs were selected to build the risk score model through the least absolute shrinkage and selection operator (LASSO) regression. Every patient got a risk score (RS), and patients were separated into a high-risk group and a low-risk group due to the median RS. The multivariate Cox analysis showed that the RS could be an independent prognosis predictor. The Kaplan-Meier survival analysis and the Receiver Operating Characteristic (ROC) curve indicated the model had an excellent prediction effect. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the mRNAs in the prognostic network were mainly involved in the autophagy and ubiquitin-like protein ligase binding. Gene Set Enrichment Analysis (GSEA) analysis uncovered that the differentially expressed genes (DEGs) in the high-risk group partially participated in the ECM receptor interaction and other signaling pathways. Our results indicated that the risk score model based on the autophagy-related lncRNAs performed well in the prediction of prognosis for patients with GC.
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